code: def user_linear_equation(Y, YtY, Cui, u, regularization, n_factors):
      # Xu = (YtCuY + regularization * I)^-1 (YtCuPu)
      # YtCuY + regularization * I = YtY + regularization * I + Yt(Cu-I)
  
      # accumulate YtCuY + regularization*I in A
      A = YtY + regularization * np.eye(n_factors)
  
      # accumulate YtCuPu in b
      b = np.zeros(n_factors)
  
      for i, confidence in nonzeros(Cui, u):
          factor = Y[i]
  
          if confidence > 0:
              b += confidence * factor
          else:
              confidence *= -1
  
          A += (confidence - 1) * np.outer(factor, factor)
      return A, b
  
imports: import functools
  import heapq
  import logging
  import time
  import numpy
  import scipy
  import scipy.sparse
  from tqdm.auto import tqdm
  from ..utils import check_blas_config, check_csr, check_random_state, nonzeros
  from .None import _als
  from .matrix_factorization_base import MatrixFactorizationBase
label_desc: Use image node_id 2 for calling a global function with example usage: user_linear_equation(Y, YtY, Cui, u, regularization, n_factors) and returns: A,  b